Stochastic Fuzzy Control Research Articles

Robust stability analysis for class of Takagi-Sugeno (T-S) fuzzy with stochastic process for sustainable hypersonic vehicles

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics.

A Stochastic Approach to Fuzzy Control

The paper presents the utilization of low-resolution data for control purposes. The control is based on fuzzy logic, with the deployment of stochastic digital low-resolution time arrays. Every control decision contains a degree of imprecision, being derived from measured low-resolution data. The imprecision is eliminated by stochastic noise superimposed during the data gathering, while the negative effects of noise are suppressed both by the fuzzy nature of the decision-making process and by the energy inertia in the controlled object. The proposed stochastic fuzzy control is extremely fast, robust and so simple that it practically does not need a microprocessor. This approach is validated by a simulation of holding upright an inverse pendulum.

Robust Fuzzy Tracking Control Design for a Class of Nonlinear Stochastic Markovian Jump Systems

This paper deals with the problem of robust fuzzy tracking control design for a class of nonlinear stochastic Itô-type systems with Markovian jumps. Considering the fuzzy approximation errors as norm-bounded uncertainties, we derive two sufficient conditions for the nonlinear stochastic robust fuzzy tracking control in terms of coupled matrix inequalities, which ensure the globally asymptotical stability in probability and L2 property for the augmented system, respectively. Then, a systematic algorithm is developed to construct the robust fuzzy tracking controller by reformulating the coupled matrix inequalities into two intertwined linear matrix inequalities (LMIs). Finally, a simulation example is presented to illustrate the design procedure.

Electric powertrain modeling of a fuel cell hybrid electric vehicle and development of a power distribution algorithm based on driving mode recognition

This paper proposes a novel fuzzy controller based on an adaptive membership function for optimum power management of a fuel cell hybrid electric vehicle (FCHEV). In the first phase, an electric powertrain model of the FCHEV is derived and a fuzzy controller is proposed. Then, the fuzzy controller is optimized using a genetic algorithm. The optimization process is accomplished through simulation for a given driving cycle. Since, however, the optimized result may vary according to the applied driving cycle for optimization, it is impossible for one optimized result to cover various driving cycles. In the second phase, an adaptive membership function based on a stochastic approach is proposed to guarantee optimum performance from the presented fuzzy controller, even though the driving cycle changes. This controller is referred to as the ‘Stochastic fuzzy controller’ (SFC) in this study. The SFC employs a stochastic approach where membership functions can be transformed statistically using a probability evaluated from driving pattern recognition. Then, driving cycle analysis is performed through off-line simulation and hardware in a loop simulation (HILS) test for four driving cycles. Finally, the SFC shows the best performance in terms of minimum fuel consumption and state-of-charge (SoC) maintenance.

Robust $H_{\infty}$ Fuzzy Control Approach for a Class of Markovian Jump Nonlinear Systems

This paper addresses the problem of stabilizing a class of nonlinear systems subject to Markovian jump parameters using a robust stochastic fuzzy controller with H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> performance. The class of jump nonlinear systems considered is described by a fuzzy model composed of two levels: A crisp level which represents the jumps and a fuzzy level which represents the system nonlinearities. Considering the approximation error between the fuzzy model and the jump nonlinear system as norm-bounded uncertainties, we develop a systematic technique based on a coupled Lyapunov function to obtain a robust stochastic fuzzy controller which guarantees the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> gain of the closed-loop system in respect to external inputs to be equal to or less than a prescribed value. A simulation example on an industrial power plant operating in a cogeneration scheme is presented to illustrate the effectiveness of the proposed stabilizing controller in reducing oscillations as well as maintaining a desired operation condition in the presence of fluctuations in the local load

Rapid dendritic growth investigated with artificial neural network method

Rapid dendritic growth of γ-(Ni, Fe) phase, β-CoSb intermetallic compound and α-Fe phase was realized by undercooling Ni-10%Fe single phase alloy, Co-60.5%Sb intermetallic alloy and Fe-40%Sn hypomonotectic alloy to a substantial extent. Their experimentally measured dendrite growth velocities were 79.5m/s, 12m/s and 0.705m/s, corresponding to undercooling levels of 303K(0.18TL), 168K(0.11 TL) and 219K(0.15 TL) respectively. Since the usual dendrite growth theory deviates significantly from reality at great undercoolings, an artificial neural network incorporated with stochastic fuzzy control was developed to explore rapid dendrite growth kinetics. It leads to the reasonable prediction that dendritic growth always exhibits a maximum velocity at a certain undercooling, beyond which dendrite growth slows down as undercooling increases still further. In the case of Fe-Sn monotectic alloys, α-Fe dendrite growth velocity was found to depend mainly on undercooling rather than alloy composition.

Stochastic Fuzzy Control. 5th Report. Dynamic Fuzzy Servo System and Its Application to an Omnidirectional Mobile Robot.

A fuzzy servo system is described for a system with noises by using a stochastic fuzzy control method with some dynamic linear models. The present fuzzy servo approach is applied to the control problem of rotational angle for an omnidirectional mobile robot with three orthogonal-wheel assemblies. For the dynamic linear system with system and measurement noises, the optimal typel servo system is first derived by applying the estimate of Kaiman filter. Next, a multiple model servo system is considered under the hyposeses on the uncertainties of initial state and input distribution matrix. Furthermore, a stochastic fuzzy servo system taking account of dynamic models is derived by introducing the equality distribution on each multiple model. Finally, the stochastic fuzzy servo method and its modified method with a static evaluation on the model confidence are demonstrated through some simulations for the mobile robot.

Stochastic Fuzzy Control. 4th Report. Position Control of Prismatic Link Using a Robust Servo Structure.

A stochastic fuzzy control method is used to construct a robust servo controller that can remove a steady-state tracking error. The present method is applied to the position control problem of a prismatic link, where the position is assumed to be measured by using a linear potentiometer with a mesurement noise. It is shown from some computer simulations that although the pressent controller basically consists of 1-type servo structure, it is very robust against not only a time-varying distrubance but also random noise, compared to a conventional robust controller.

ゴム人工筋６自由度パラレルリンクマニピュレータの積分型確率ファジィ制御

A robust control approach is described for controlling the position and orientation of a six-DOF parallel link manipulator, in which one prismatic link consists of a rubber muscle actuator, so that the controlled object contains several nonlinear factors. The position of the link is also assumed to be measured by using a linear potentiometer. As a result, the associated measurement system contains some measurement noises. The control system is construted using a stochastic fuzzy controller with an integlator so as to remove a tracking error. To design the feedback gains of the stochastic fuzzy controller, a mmn-max control strategy is adopted to the systems, with unknown physical parameters. The effectiveness of the proposed method is illustrated by using some actual experiments.

線形min-max制御としての確率ファジィ制御器設計

線形min-max制御としての確率ファジィ制御器設計

Trajectory Tracking Control for a Mobile Robot Using Stochastic Fuzzy Controller

This paper presents a new design for a fuzzy controller, called ""stochastic fuzzy control."" It describes the application controlling a mobile robot driven by two independent wheels. Using this scheme, the fuzzy control system provides an optimal stochastic control strategy that ensures the stability of the control system. Two design methods are considered: one assumes that the control object is completely known, and the other assumes that the control object includes unknown parameters. We design the straight line and circular path reference trajectories in accordance with the practical applications for the mobile robot, in which three patterns of speed are adopted. The effectiveness of the proposed method is demonstrated by a series of practical tests using our experimental mobile robot.

Stochastic Fuzzy Control. (Theoretical Derivation).

Stochastic fuzzy control using stochastic control theory, instead of using conventional fuzzy reasoning, is proposed. We first solve a control problem of one-step predictive output tracking for linear stochastic systems. Next, we consider dynamic multiple model adaptive control(MMAC)for the initial data distribution, under the uncertainties of the initial states. We further consider static MMAC that can be applied for cases of completely unknown plants. It is then shown that a stochastic fuzzy control has some Gaussian potential functions as membership functions and can be used to assign some a priori probabilities to the fuzzy sets or to the control rules, if the probability density function with respect to the output error is replaced by a simple characteristic function. It is also shown that the stochastic fuzzy contol becomes fuzzy control, if all of the a priori probabilities are set to be equal at any control instant.

Stochastic Fuzzy Control. 1st Report. Theoretical Derivation.

A stochastic fuzzy control is proposed by applying the stochastic control theory, instead of using conventional fuzzy reasoning. We first solve a control problem of one-step predicted output tracking for linear stochastic systems. We then derive a dynamic multiple model adaptive control (MMAC) for the initial data distribution, given the uncertainties of the initial states. We further consider a static MMAC that can be applied to the case of completely unknown plants. It is shown that a stochastic fuzzy control has some Gaussian potential functions as membership functions and can assign some a priori probabilities to the fuzzy sets or to the control rules, if the probability density function with respect to the output error is replaced by a simple characteristic function. It is also clarified that the stochastic fuzzy control becomes a conventional fuzzy control by assuming that all of the a priori probabilities are equal at any control instant.

Stochastic Fuzzy Control. 3rd Report. Application to Trajectory Tracking Control of a Mobile Robot.

A stochastic fuzzy control is applied for the trajectory tracking control of a mobile robot with two independent driving wheels. In this approach, the fuzzy control system can be realized as a stochastic control. That is, the control system mainly consists of two parts: one is an estimation part which estimates the initial state by using a static fuzzy observer; while the other is a feedback control part consisting of a constant feedback gain and the estimated initial state. When the control object is assumed to be unknown, the control gain can be designed from the viewpoint of a variable structure system (VSS) control theory. On the other hand, when the control object is assumed to be known, the control gain can be easily designed from the viewpoint of a pole assignment. It is also pointed out that uncontrollable state mode data are not necessary for constructing the stochastic fuzzy controllers. The effectiveness of the proposed method is illustrated using computer simulations.

Stochastic Fuzzy Control. 2nd Report. Relationships Among A Priori Probabilities, Fuzzy Sets and Control Rules.

The role of the a priori probabilities is unclear for a stochastic fuzzy control with a single vector data described In the first report, because in such a case the number of Gaussian potential functions is equal to the number of Control rules. In addition, as in a conventional fuzzy control based on fuzzy reasoning, it is necessary to clarify the relationships between the fuzzy sets and the control rules. We here consider a static multiple model adaptive control (MMAC) for the initial data distribution, in which it is assumed that a single vector data is partitioned into two subvector data and the hierarchical hypotheses are introduced with respect to each initial data distribution. Then, two kinds of stochastic fuzzy Control are proposed on the basis of such a static MMAC: one Case assigns the a priori probabilities to the fuzzy sest for each subvector data, and the other case assigns the a priori probabilities to the control rules.